CosyVoice README

The project's README from github.com/QwenAudio/CosyVoice. As one markdown file: README.md.

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👉🏻 CosyVoice 👈🏻

Fun-CosyVoice 3.0: Demos; Paper; Modelscope; Huggingface; CV3-Eval

CosyVoice 2.0: Demos; Paper; Modelscope; HuggingFace

CosyVoice 1.0: Demos; Paper; Modelscope; HuggingFace

Highlight🔥

Fun-CosyVoice 3.0 is an advanced text-to-speech (TTS) system based on large language models (LLM), surpassing its predecessor (CosyVoice 2.0) in content consistency, speaker similarity, and prosody naturalness. It is designed for zero-shot multilingual speech synthesis in the wild.

Key Features

Roadmap

Evaluation

Model Open-Source Model Size test-zh
CER (%) ↓		 test-zh
SS (%) ↑		 test-en
WER (%) ↓		 test-en
SS (%) ↑		 test-hard
CER (%) ↓		 test-hard
SS (%) ↑
Human - - 1.26 75.5 2.14 73.4 - -
Seed-TTS ❌ - 1.12 79.6 2.25 76.2 7.59 77.6
MiniMax-Speech ❌ - 0.83 78.3 1.65 69.2 - -
F5-TTS ✅ 0.3B 1.52 74.1 2.00 64.7 8.67 71.3
Spark TTS ✅ 0.5B 1.2 66.0 1.98 57.3 - -
CosyVoice2 ✅ 0.5B 1.45 75.7 2.57 65.9 6.83 72.4
FireRedTTS2 ✅ 1.5B 1.14 73.2 1.95 66.5 - -
Index-TTS2 ✅ 1.5B 1.03 76.5 2.23 70.6 7.12 75.5
VibeVoice-1.5B ✅ 1.5B 1.16 74.4 3.04 68.9 - -
VibeVoice-Realtime ✅ 0.5B - - 2.05 63.3 - -
HiggsAudio-v2 ✅ 3B 1.50 74.0 2.44 67.7 - -
VoxCPM ✅ 0.5B 0.93 77.2 1.85 72.9 8.87 73.0
GLM-TTS ✅ 1.5B 1.03 76.1 - - - -
GLM-TTS RL ✅ 1.5B 0.89 76.4 - - - -
Fun-CosyVoice3-0.5B-2512 ✅ 0.5B 1.21 78.0 2.24 71.8 6.71 75.8
Fun-CosyVoice3-0.5B-2512_RL ✅ 0.5B 0.81 77.4 1.68 69.5 5.44 75.0

Install

Clone and install

Model download

We strongly recommend that you download our pretrained Fun-CosyVoice3-0.5B CosyVoice2-0.5B CosyVoice-300M CosyVoice-300M-SFT CosyVoice-300M-Instruct model and CosyVoice-ttsfrd resource.

# modelscope SDK model download
from modelscope import snapshot_download
snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B')
snapshot_download('iic/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
snapshot_download('iic/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT')
snapshot_download('iic/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct')
snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')

# for oversea users, huggingface SDK model download
from huggingface_hub import snapshot_download
snapshot_download('FunAudioLLM/Fun-CosyVoice3-0.5B-2512', local_dir='pretrained_models/Fun-CosyVoice3-0.5B')
snapshot_download('FunAudioLLM/CosyVoice2-0.5B', local_dir='pretrained_models/CosyVoice2-0.5B')
snapshot_download('FunAudioLLM/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M')
snapshot_download('FunAudioLLM/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT')
snapshot_download('FunAudioLLM/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct')
snapshot_download('FunAudioLLM/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd')

Optionally, you can unzip ttsfrd resource and install ttsfrd package for better text normalization performance.

Notice that this step is not necessary. If you do not install ttsfrd package, we will use wetext by default.

cd pretrained_models/CosyVoice-ttsfrd/
unzip resource.zip -d .
pip install ttsfrd_dependency-0.1-py3-none-any.whl
pip install ttsfrd-0.4.2-cp310-cp310-linux_x86_64.whl

Basic Usage

We strongly recommend using Fun-CosyVoice3-0.5B for better performance. Follow the code in example.py for detailed usage of each model.

python example.py

vLLM Usage

CosyVoice2/3 now supports vLLM 0.11.x+ (V1 engine) and vLLM 0.9.0 (legacy). Older vllm version(<0.9.0) do not support CosyVoice inference, and versions in between (e.g., 0.10.x) are not tested.

Notice that vllm has a lot of specific requirements. You can create a new env to in case your hardward do not support vllm and old env is corrupted.

conda create -n cosyvoice_vllm --clone cosyvoice
conda activate cosyvoice_vllm
# for vllm==0.9.0
pip install vllm==v0.9.0 transformers==4.51.3 numpy==1.26.4 -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
# for vllm>=0.11.0
pip install vllm==v0.11.0 transformers==4.57.1 numpy==1.26.4 -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com
python vllm_example.py

Start web demo

You can use our web demo page to get familiar with CosyVoice quickly.

Please see the demo website for details.

# change iic/CosyVoice-300M-SFT for sft inference, or iic/CosyVoice-300M-Instruct for instruct inference
python3 webui.py --port 50000 --model_dir pretrained_models/CosyVoice-300M

Advanced Usage

For advanced users, we have provided training and inference scripts in examples/libritts.

Build for deployment

Optionally, if you want service deployment, You can run the following steps.

cd runtime/python
docker build -t cosyvoice:v1.0 .
# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference
# for grpc usage
docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/grpc && python3 server.py --port 50000 --max_conc 4 --model_dir iic/CosyVoice-300M && sleep infinity"
cd grpc && python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>
# for fastapi usage
docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && python3 server.py --port 50000 --model_dir iic/CosyVoice-300M && sleep infinity"
cd fastapi && python3 client.py --port 50000 --mode <sft|zero_shot|cross_lingual|instruct>

Using Nvidia TensorRT-LLM for deployment

Using TensorRT-LLM to accelerate cosyvoice2 llm could give 4x acceleration comparing with huggingface transformers implementation. To quick start:

cd runtime/triton_trtllm
docker compose up -d

For more details, you could check here

Discussion & Communication

You can directly discuss on Github Issues.

You can also scan the QR code to join our official Dingding chat group.

Acknowledge

  1. We borrowed a lot of code from FunASR.
  2. We borrowed a lot of code from FunCodec.
  3. We borrowed a lot of code from Matcha-TTS.
  4. We borrowed a lot of code from AcademiCodec.
  5. We borrowed a lot of code from WeNet.

Citations

@article{du2024cosyvoice,
  title={Cosyvoice: A scalable multilingual zero-shot text-to-speech synthesizer based on supervised semantic tokens},
  author={Du, Zhihao and Chen, Qian and Zhang, Shiliang and Hu, Kai and Lu, Heng and Yang, Yexin and Hu, Hangrui and Zheng, Siqi and Gu, Yue and Ma, Ziyang and others},
  journal={arXiv preprint arXiv:2407.05407},
  year={2024}
}

@article{du2024cosyvoice,
  title={Cosyvoice 2: Scalable streaming speech synthesis with large language models},
  author={Du, Zhihao and Wang, Yuxuan and Chen, Qian and Shi, Xian and Lv, Xiang and Zhao, Tianyu and Gao, Zhifu and Yang, Yexin and Gao, Changfeng and Wang, Hui and others},
  journal={arXiv preprint arXiv:2412.10117},
  year={2024}
}

@article{du2025cosyvoice,
  title={CosyVoice 3: Towards In-the-wild Speech Generation via Scaling-up and Post-training},
  author={Du, Zhihao and Gao, Changfeng and Wang, Yuxuan and Yu, Fan and Zhao, Tianyu and Wang, Hao and Lv, Xiang and Wang, Hui and Shi, Xian and An, Keyu and others},
  journal={arXiv preprint arXiv:2505.17589},
  year={2025}
}

@inproceedings{lyu2025build,
  title={Build LLM-Based Zero-Shot Streaming TTS System with Cosyvoice},
  author={Lyu, Xiang and Wang, Yuxuan and Zhao, Tianyu and Wang, Hao and Liu, Huadai and Du, Zhihao},
  booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1--2},
  year={2025},
  organization={IEEE}
}

Ecosystem

CosyVoice is part of the FunAudioLLM family — a complete speech AI toolkit:

Project Description Stars
FunASR Industrial speech recognition — 50+ languages, speaker diarization, streaming
Fun-ASR-Nano End-to-end LLM-based ASR — 31 languages, hotwords, vLLM streaming
SenseVoice Ultra-fast ASR + emotion + audio event detection
FunClip AI video clipping powered by speech recognition

Disclaimer

The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. If any content infringes on your rights, please contact us to request its removal.

CosyVoice (QwenAudio/CosyVoice)

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24K ·GitHub
CosyVoice
What is this?CosyVoice is Alibaba's open-source, LLM-based text-to-speech system. It generates natural speech in 9 languages plus 18+ Chinese dialects, supports zero-shot voice cloning, and ships inference, training and deployment code.
Is this a model I download, or a toolkit I run myself?Both: the repo provides pretrained checkpoints (Fun-CosyVoice3-0.5B recommended, plus CosyVoice2-0.5B and the 300M series) and the Python code to run, fine-tune and serve them.
How do I get it running?Clone the repo, install Conda and create a Conda environment, download a pretrained model — Fun-CosyVoice3-0.5B is recommended — then follow example.py for usage of each model.
Any setup gotchas I should know about?The ttsfrd resource improves text normalization but is optional — without it the repo falls back to the wetext package by default. Also expect sizable model downloads, since the current models are 0.5B-class.
Can I use it in commercial products?The code is Apache-2.0, but the README explicitly says the provided content is for academic purposes and to demonstrate technical capabilities — check the model terms before shipping anything commercial.
Which checkpoint should I start with?Fun-CosyVoice3-0.5B — the README recommends it over CosyVoice2-0.5B and the older 300M series (Base, SFT, Instruct). A 2512 reinforcement-learning variant, Fun-CosyVoice3-0.5B-2512RL, tops the project's own eval table.
How does it stack up against other open-source TTS models?Per the README benchmark table, Fun-CosyVoice3-0.5B-2512RL reaches 0.81% CER on test-zh and 5.44% on the hard test set with 0.5B parameters — better error rates than F5-TTS, Spark TTS, Index-TTS2 and GLM-TTS in the same table.
What else is in the FunAudioLLM family?CosyVoice is the speech-generation side of the FunAudioLLM toolkit, alongside FunASR (industrial speech recognition in 50+ languages), SenseVoice (ultra-fast ASR plus emotion and audio event detection) and FunClip (AI video clipping driven by speech recognition).
What are similar projects?ChatTTS, VoxCPM, MOSS-TTS-Nano, MOSS-TTS, index-tts, OpenVoice, ebook2audiobook, dia
Which topics is it in?tts-model, zero-shot-tts, gpt-4o, voice-conversion, alibaba, korean, japanese, audio-generation, english, voice-cloning, speech-synthesis, fastapi
Creator
QwenAudio
Size
2.7 MB
Stars
24K
Updated
CommitMay 25
Age
2y
Commits · 90 Days